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William Greene Stern School of Business New York University. Efficiency Measurement. Lab Session 2. Stochastic Frontier Estimation. Application to Spanish Dairy Farms. N = 247 farms, T = 6 years (1993-1998). Using Farm Means of the Data. OLS vs. Frontier/MLE. JLMS Inefficiency Estimator.

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william greene stern school of business new york university
William Greene

Stern School of Business

New York University

Efficiency Measurement
lab session 2

Lab Session 2

Stochastic Frontier Estimation

application to spanish dairy farms
Application to Spanish Dairy Farms

N = 247 farms, T = 6 years (1993-1998)

jlms inefficiency estimator
JLMS Inefficiency Estimator

FRONTIER ; LHS = the variable

; RHS = ONE, the variables

; EFF = the new variable $

Creates a new variable in the data set.

FRONTIER ; LHS = YIT ; RHS = X ; EFF = U_i $

Use ;Techeff = variable to compute exp(-u).

cost frontier command
Cost Frontier Command

FRONTIER ; COST

; LHS = the variable

; RHS = ONE, the variables

; EFF = the new variable $

ε(i) = v(i) + u(i) [u(i) is still positive]

normal truncated normal frontier command
Normal-Truncated NormalFrontier Command

FRONTIER [; COST]

; LHS = the variable

; RHS = ONE, the variables

; Model = Truncation

; EFF = the new variable $

ε(i) = v(i) +/- u(i)

u(i) = |U(i)|, U(i) ~ N[μ,2]

The half normal model has μ = 0.

observations
Observations
  • Truncation Model estimation is often unstable
    • Often estimation is not possible
    • When possible, estimates are often wild
  • Estimates of u(i) are usually only moderately affected
  • Estimates of u(i) are fairly stable across models (exponential, truncation, etc.)
ranking observations
Ranking Observations

CREATE ; newname = Rnk ( Variable ) $

Creates the set of ranks. Use in any subsequent analysis.

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